_base_ = "../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py"
model = dict(
    neck=dict(
        type="FPN_CARAFE",
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5,
        start_level=0,
        end_level=-1,
        norm_cfg=None,
        act_cfg=None,
        order=("conv", "norm", "act"),
        upsample_cfg=dict(
            type="carafe",
            up_kernel=5,
            up_group=1,
            encoder_kernel=3,
            encoder_dilation=1,
            compressed_channels=64,
        ),
    ),
    roi_head=dict(
        mask_head=dict(
            upsample_cfg=dict(
                type="carafe",
                scale_factor=2,
                up_kernel=5,
                up_group=1,
                encoder_kernel=3,
                encoder_dilation=1,
                compressed_channels=64,
            )
        )
    ),
)
img_norm_cfg = dict(
    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True
)
train_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(type="LoadAnnotations", with_bbox=True, with_mask=True),
    dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
    dict(type="RandomFlip", flip_ratio=0.5),
    dict(type="Normalize", **img_norm_cfg),
    dict(type="Pad", size_divisor=64),
    dict(type="DefaultFormatBundle"),
    dict(type="Collect", keys=["img", "gt_bboxes", "gt_labels", "gt_masks"]),
]
test_pipeline = [
    dict(type="LoadImageFromFile"),
    dict(
        type="MultiScaleFlipAug",
        img_scale=(1333, 800),
        flip=False,
        transforms=[
            dict(type="Resize", keep_ratio=True),
            dict(type="RandomFlip"),
            dict(type="Normalize", **img_norm_cfg),
            dict(type="Pad", size_divisor=64),
            dict(type="ImageToTensor", keys=["img"]),
            dict(type="Collect", keys=["img"]),
        ],
    ),
]
data = dict(
    train=dict(pipeline=train_pipeline),
    val=dict(pipeline=test_pipeline),
    test=dict(pipeline=test_pipeline),
)
